Fraction-Score: A New Support Measure for Co-location Pattern Mining

PhD Thesis Proposal Defence
Title: "Fraction-Score: A New Support Measure for Co-location Pattern
Mining"
by
Mr. Kai Ho CHAN
Abstract:
Co-location patterns are well-established on spatial objects with
categorical labels, which capture the phenomenon that objects with certain
labels are often located in close geographic proximity. Similar to
frequent itemsets, co-location patterns are defined based on a support
measure which quantifies the popularity (or prevalence) of a pattern
candidate (a label set). Quite a few support measures exist for defining
co-location patterns and they share an idea of counting the number of
instances of a given label set C as its support, where an instance of C is
an object set whose objects carry all the labels in C and are located
close to one another. Unfortunately, these measures suffer from various
weaknesses, e.g., some fail to capture all possible instances while some
others overlook the cases when multiple instances overlap. In this thesis,
we propose a new measure called Fraction-Score whose idea is to count
instances fractionally if they overlap. Compared to existing measures,
Fraction-Score not only captures all possible instances, but also handles
the cases where instances overlap appropriately (so that the supports
defined are more meaningful and consistent with the desirable
anti-monotonicity property). To solve the co-location pattern mining
problem based on Fraction-Score, we develop efficient algorithms which are
significantly faster than a baseline that adapts the state-of-the-art. We
conduct extensive experiments using both real and synthetic datasets,
which verified the superiority of Fraction-Score and also the efficiency
of our developed algorithms.
Date: Wednesday, 19 December 2018
Time: 4:00pm - 6:00pm
Venue: Room 2131C
(lift 19)
Committee Members: Dr. Raymond Wong (Supervisor)
Prof. Dimitris Papadias (Chairperson)
Prof. Gary Chan
Prof. Dit-Yan Yeung
**** ALL are Welcome ****